ATLAS: Learning to Optimally Memorize the Context at Test Time

TL;DR

ATLAS introduces a novel long-term memory module that enhances context memorization in sequence models, surpassing Transformers and recurrent models in long-context tasks, with significant improvements demonstrated across various benchmarks.

Contribution

The paper proposes ATLAS, a high-capacity memory module that learns to memorize context by optimizing memory with current and past tokens, and introduces DeepTransformers, a new family of Transformer-like architectures.

Findings

ATLAS outperforms Transformers and recurrent models in long-context tasks.

Achieves +80% accuracy on 10M context length in BABILong benchmark.

Improves long-term context understanding in various language tasks.

Abstract

Transformers have been established as the most popular backbones in sequence modeling, mainly due to their effectiveness in in-context retrieval tasks and the ability to learn at scale. Their quadratic memory and time complexity, however, bound their applicability in longer sequences and so has motivated researchers to explore effective alternative architectures such as modern recurrent neural networks (a.k.a long-term recurrent memory module). Despite their recent success in diverse downstream tasks, they struggle in tasks that requires long context understanding and extrapolation to longer sequences. We observe that these shortcomings come from three disjoint aspects in their design: (1) limited memory capacity that is bounded by the architecture of memory and feature mapping of the input; (2) online nature of update, i.e., optimizing the memory only with respect to the last input; and (3) less expressive management of their fixed-size memory. To enhance all these three aspects, we present ATLAS, a long-term memory module with high capacity that learns to memorize the context by optimizing the memory based on the current and past tokens, overcoming the online nature of long-term memory models. Building on this insight, we present a new family of Transformer-like architectures, called DeepTransformers, that are strict generalizations of the original Transformer architecture. Our experimental results on language modeling, common-sense reasoning, recall-intensive, and long-context understanding tasks show that ATLAS surpasses the performance of Transformers and recent linear recurrent models. ATLAS further improves the long context performance of Titans, achieving +80\% accuracy in 10M context length of BABILong benchmark.